Fig. 2
From: Dictionary learning allows model-free pseudotime estimation of transcriptomic data
Correlation evaluations for the simulated datasets for each evaluated method. The subfigures shows evaluations for each method (method name in subtitle). The x-axis shows the number of genes with the simulated time pattern, |gsim|. The y-axis shows for each dataset the maximum correlations among the matrices with different method parameters. The dataset with one half of |gsim| increasingly ordered and the other half fluctuating is labelled “Incr_Fluct”, with “Incr_Fluct_1” being the half of increasing values and “Incr_Fluct_2” the half of fluctuating values. The high noise perturbation is labelled Noise+. Correlations for DiL, ICA, and NMF increase or remain similar for increasing |gsim|, which presents an anticipated behaviour. However, compared to DiL and ICA, correlations for NMF are high for higher values of |gsim| only. For PCA, t-SNE, and UMAP this property is generally not held